San Jose, California, United States, United States of America
Onsite
About Altera
At Altera™, our independence as the world’s largest pure‑play FPGA solutions provider gives us the focus, speed, and agility to innovate without compromise. With more than four decades of industry‑leading FPGA expertise, our singular mission is to deliver the programmable technologies that help customers differentiate, innovate, and scale across rapidly evolving markets like AI, cloud, networking, and edge. As an independent company, we move faster, invest deeper, and partner more closely—empowering our teams to drive breakthrough innovation and shape the future of the FPGA industry.
About the Role
We are looking for a Senior MLOps & AI Infrastructure Engineer to architect, build, and operationalize machine learning systems at scale. This role sits at the intersection of data science, software engineering, and infrastructure — combining deep ML expertise with the DevOps/MLOps discipline required to ship models reliably into production.
You will partner closely with software, data, and infrastructure teams to design end-to-end ML pipelines, automate model lifecycle management, and deliver AI-powered capabilities across our EDA, HPC, and cloud environments.
Key Responsibilities:
ML Platform & Pipeline Engineering
• Design, build, and maintain scalable ML pipelines for training, evaluation, and deployment across cloud and on-prem HPC environments
• Build MLOps infrastructure including experiment tracking, model registry, feature stores, and automated retraining workflows
• Implement CI/CD/CT (Continuous Training) pipelines for ML models using tools such as Kubeflow, MLflow, Airflow, or similar
• Containerize ML workloads with Docker and orchestrate at scale using Kubernetes and GPU node pools
Model Development & Optimization
• Develop, fine-tune, and deploy large-scale models including LLMs, GNNs, and reinforcement learning agents for EDA and chip design applications
• Apply advanced techniques: transfer learning, quantization, pruning, distillation, and RLHF for production-grade model efficiency
• Implement A/B testing frameworks and shadow deployments for safe model rollout
• Benchmark and optimize model inference performance on GPU/TPU clusters
Data Engineering & Feature Management
• Build and maintain data pipelines for large-scale structured and unstructured datasets (terabyte-scale)
• Collaborate with data teams to design feature engineering systems and maintain data quality for ML training
• Implement data versioning and lineage tracking (DVC, Delta Lake, or similar)
Infrastructure & Operations
• Manage cloud ML infrastructure on AWS (SageMaker), Azure (AML), or GCP (Vertex AI) with cost and performance optimization
• Automate infrastructure provisioning using Terraform or CloudFormation for GPU-backed ML environments
• Build monitoring, alerting, and observability systems for model performance drift, data quality, and system health
• Support HPC schedulers (LSF, Slurm) for large-scale distributed training jobs
Collaboration & Leadership
• Partner with research scientists to productionize experimental models with engineering rigor
• Mentor junior engineers and define ML engineering best practices across the organization
• Drive adoption of AI/ML solutions within semiconductor, EDA, and simulation workflows
Technology Stack
ML Frameworks:
PyTorch • TensorFlow • JAX • Hugging Face • scikit-learn • XGBoost
MLOps & Pipelines:
MLflow • Kubeflow • Airflow • Weights & Biases • DVC • Feast
Infrastructure & Cloud:
AWS SageMaker / GCP Vertex AI / Azure ML • Terraform • Docker • Kubernetes • Slurm / LSF
Languages:
Python • Bash • Go • SQL
Monitoring & Observability:
Prometheus • Grafana • ELK Stack • Evidently AI • Arize
Key Competencies
• Strong ownership mindset — you drive ML initiatives from prototype to production without being asked
• Bias toward automation: if you do it twice, you automate it
• Ability to bridge research and engineering — translating papers into production-grade systems
• Thrives in fast-paced, ambiguous environments typical of deep-tech and semiconductor companies
• Clear communicator who can explain complex ML concepts to non-technical stakeholders
Salary Range
The pay range below is for Bay Area California only. Actual salary may vary based on a number of factors including job location, job-related knowledge, skills, experiences, trainings, etc. We also offer incentive opportunities that reward employees based on individual and company performance.
$149,100 - $215,925 USD
We use artificial intelligence to screen, assess, or select applicants for the position. Applicants must be eligible for any required U.S. export authorizations.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or related field and 10+ years of industry experience
10+ years of experience across ML engineering, data science, and MLOps — including frameworks (PyTorch, TensorFlow, JAX, Hugging Face) and production model deployment at scale
8+ years of experience experience with parallelism strategies (FSDP, DeepSpeed, data/model parallelism)
10+ years of experience and proficiency in Python programming
8+ years of experience in cloud ML platforms (AWS, GCP, Azure), Docker/Kubernetes, and CI/CD pipelines
5+ years of hands-on experience with MLflow, W&B, or Neptune for tracking and reproducibility
Preferred Qualifications
Phd in Computer Science, Machine Learning, Statistics, or related field
Experience applying ML/AI to semiconductor, EDA, or chip design domains (e.g., timing prediction, place & route optimization, DRC closure)
Familiarity with HPC schedulers such as LSF or Slurm and GPU cluster management for training workloads
Knowledge of LLM fine-tuning, Retrieval-Augmented Generation (RAG) architectures, and AI agent frameworks such as LangChain or AutoGen
Experience with graph neural networks (GNNs) or geometric deep learning for circuit and netlist analysis
Background in reinforcement learning for optimization problems
Exposure to zero-trust security, DevSecOps, and compliance automation for ML systems
Experience working with large-scale simulation pipelines and synthetic data generation
Experience at organizations such as NVIDIA, AMD, Intel, Google DeepMind, or similar AI/HPC-focused companies
Published research or open-source contributions in ML, MLOps, or AI for EDA
Experience building AI-powered developer tools or copilot-style products
Familiarity with Synopsys, Cadence, or Siemens EDA toolchains and associated data formats